Structural repairs alter the physical properties of a structure, changing its responses, both in terms of its normal condition and of its different damage states. This difference in responses manifests itself as a shift between the pre- and post-repair data distributions, which can be problematic for conventional data-driven approaches to structural health monitoring (SHM), and limits their effectiveness in industrial applications. This limitation occurs typically because approaches assume that the data distribution is the same in training as appears in testing; with an algorithm failing to generalise when this assumption is not true; that is, pre-repair labels no longer apply to the post-repair data. Transfer learning, in the form of domain adaptation, proposes a solution to this issue, by mapping the pre- and post-repair data distributions onto a shared latent space where their distributions are approximately equal, allowing pre-repair label knowledge to be used to classify the post-repair data. This paper demonstrates the applicability of domain adaptation as a method for overcoming the problem of repair on a dataset from a Gnat trainer aircraft. In addition, a novel modification to an existing domain adaptation technique – joint distribution adaptation – is proposed, which seeks to improve the semi-supervised learning phase of the algorithm by considering a metric-informed procedure. The metric-informed joint distribution adaptation algorithm is benchmarked against, and shown to outperform, both conventional data-based approaches and other domain adaptation techniques.

结构修复改变了结构的物理特性，改变了其正常状态和不同损坏状态的响应。这种响应差异本身表现为修复前和修复后数据分布之间的转变，这对于结构健康监测 (SHM) 的传统数据驱动方法可能存在问题，并限制了它们在工业应用中的有效性。这种限制的发生通常是因为方法假设训练中的数据分布与测试中的数据分布相同；当这个假设不成立时，算法无法概括；也就是说，修复前的标签不再适用于修复后的数据。迁移学习，以领域适应的形式，提出了这个问题的解决方案，通过将修复前和修复后数据分布映射到共享的潜在空间，其中它们的分布近似相等，从而允许使用修复前标签知识对修复后数据进行分类。本文展示了域自适应作为克服 Gnat 教练机数据集修复问题的方法的适用性。此外，还提出了对现有域适应技术——联合分布适应——的一种新颖修改，该技术旨在通过考虑度量通知程序来改进算法的半监督学习阶段。度量通知联合分布自适应算法以传统的基于数据的方法和其他域自适应技术为基准，并显示其性能优于传统的基于数据的方法和其他域自适应技术。